Abstract: An electronic apparatus is provided. The electronic apparatus includes a communication interface, a memory storing log data with respect to external devices connected to the electronic apparatus, and a processor configured to identify a plurality of external devices having a history of being connected to the same internet protocol (IP) based on the log data, acquire, based on the log data, a first feature vector with respect to a relationship between the plurality of external devices and a second feature vector with respect to each of the plurality of external devices, acquire a graph of the relationship between the plurality of external devices based on the first feature vector and the second feature vector, and define at least one group configured by the plurality of external devices based on the graph.

Abstract: An electronic apparatus is provided. The electronic apparatus includes a memory configured to store an entity identification model, and a processor configured to control the electronic apparatus to: identify a plurality of entities included in an input sentence input based on the entity identification model, acquire a search result corresponding to the plurality of entities, and correct and provide the input sentence based on the search result based on the plurality of entities not corresponding to the search result, wherein the entity identification model may be acquired by learning through an artificial intelligence algorithm to extract a plurality of sample entities included in each of a plurality of sample dialogues. At least a part of a method for identifying an entity from a dialogue may use an artificial intelligence model learned according to at least one of machine learning, a neural network, deep learning algorithm, or the like.

Abstract: An electronic device is provided. The electronic device includes a memory configured to store a computer executable instructions; and a processor configured to execute the executable instructions to: determine a text corresponding to a received command, provide response information on the command based on a first artificial intelligence model classifying the text as a text corresponding to one of a plurality of pre-stored texts, and provide error information on the command based on the first artificial intelligence model classifying the text as an error, wherein the first artificial intelligence model is configured to classify the text as the error based on the text corresponding to the command being a similar text having one of an entity and an intent different from at least one of the plurality of pre-stored texts.

Abstract: Provided are a first device and a method of operating thereof. The first device includes: a communicator comprising communication circuitry; a memory configured to store one or more instructions; and a processor configured to execute the one or more instructions stored in the memory, wherein the processor is configured to control the first device to: extract sensitive information included in original content to be shared, to perform anonymization on the sensitive information to create anonymized content corresponding to the original content, to control the communicator to transmit the anonymized content to a second device, to perform, based on a request for the original content being received from the second device through the communicator, authentication in response to the request, and transmit the original content to the second device based on the authentication being completed.

Abstract: An electronic apparatus is provided. The electronic apparatus includes a memory configured to store an entity identification model, and a processor configured to control the electronic apparatus to: identify a plurality of entities included in an input sentence input based on the entity identification model, acquire a search result corresponding to the plurality of entities, and correct and provide the input sentence based on the search result based on the plurality of entities not corresponding to the search result, wherein the entity identification model may be acquired by learning through an artificial intelligence algorithm to extract a plurality of sample entities included in each of a plurality of sample dialogues. At least a part of a method for identifying an entity from a dialogue may use an artificial intelligence model learned according to at least one of machine learning, a neural network, deep learning algorithm, or the like.

Abstract: An electronic device is provided. The electronic device includes a memory configured to store a computer executable instructions; and a processor configured to execute the executable instructions to: determine a text corresponding to a received command, provide response information on the command based on a first artificial intelligence model classifying the text as a text corresponding to one of a plurality of pre-stored texts, and provide error information on the command based on the first artificial intelligence model classifying the text as an error, wherein the first artificial intelligence model is configured to classify the text as the error based on the text corresponding to the command being a similar text having one of an entity and an intent different from at least one of the plurality of pre-stored texts.

Abstract: A method of detecting an overlapping community in a network including nodes and links between the nodes, includes calculating a similarity between the links, and generating a line graph of the network. The method further includes detecting one or more cores in the line graph, and growing a cluster for each of the one or more cores. The method further includes converting the cluster into a cluster of nodes of a node graph.